A new improved Liu-type estimator for Poisson regression models

Creative Commons License

Akay K. U., Ertan E.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, vol.51, no.5, pp.1484-1503, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.15672/hujms.1012056
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH
  • Page Numbers: pp.1484-1503
  • Keywords: Poisson regression, mean squared error, multicollinearity, Ridge estimator, Liu estimator, MEAN-SQUARE ERROR, RIDGE-REGRESSION, 2-PARAMETER ESTIMATOR
  • Istanbul University Affiliated: Yes


The Poisson Regression Model (PRM) is commonly used in applied sciences such as economics and the social sciences when analyzing the count data. The maximum likelihood method is the well-known estimation technique to estimate the parameters in PRM. However, when the explanatory variables are highly intercorrelated, unstable parameter estimates can be obtained. Therefore, biased estimators are widely used to alleviate the undesirable effects of these problems. In this study, a new improved Liu-type estimator is proposed as an alternative to the other proposed biased estimators. The superiority of the new proposed estimator over the existing biased estimators is given under the asymptotic matrix mean square error criterion. Furthermore, Monte Carlo simulation studies are executed to compare the performances of the proposed biased estimators. Finally, the obtained results are illustrated in real data. Based on the set of experimental conditions which are investigated, the proposed biased estimator outperforms the other biased estimators.